From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

Gao, Shen; Chen, Xiuying; Zhang, Ren; Zhao, Dongyan; Yan, Rui

doi:10.24963/ijcai.2020/676

Cited by 35 publications

(19 citation statements)

References 8 publications

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“…We live in an information age where communications between human and human/machine are increasing exponentially in the form of textual dialogues between users and users-agents (Kester, 2004). It is challenging and time-consuming to review all the content before starting any conversations especially when the chatting history becomes very long (Gao et al, 2020). How to process and organize those interaction activities into concise and structured data, i.e.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization

Chen¹,

Yang²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

101

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Text summarization is one of the most challenging and interesting problems in NLP. Although much attention has been paid to summarizing structured text like news reports or encyclopedia articles, summarizing conversations-an essential part of humanhuman/machine interaction where most important pieces of information are scattered across various utterances of different speakersremains relatively under-investigated. This work proposes a multi-view sequence-tosequence model by first extracting conversational structures of unstructured daily chats from different views to represent conversations and then utilizing a multi-view decoder to incorporate different views to generate dialogue summaries. Experiments on a large-scale dialogue summarization corpus demonstrated that our methods significantly outperformed previous state-of-the-art models via both automatic evaluations and human judgment. We also discussed specific challenges that current approaches faced with this task. We have publicly released our code at https://github.com/GT-SALT/ Multi-View-Seq2Seq.

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Section: Introductionmentioning

confidence: 99%

Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization

Chen¹,

Yang²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

101

View full text Add to dashboard Cite

show abstract

“…In recent years, sequence-to-sequence (seq2seq) [57] based neural networks have been proved effective in generating a fluent sentence. The seq2seq model is originally proposed for machine translation and later adapted to various natural language generation tasks, such as text summarization [10,17,18,21,24,38,45,65,67] and dialogue generation [6,19,20,60,77,81,82]. Rush et al [49] apply the seq2seq mechanism with attention model to text summarization field.…”

Section: Text Generation Methodsmentioning

confidence: 99%

Meaningful Answer Generation of E-Commerce Question-Answering

Gao

Chen

Ren

et al. 2021

ACM Trans. Inf. Syst.

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In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the Meaningful Product Answer Generator ( MPAG ), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews . We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.

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“…Text Summarization. Our proposed task bases on text summarization, the methods of which can be divided into extractive and abstractive methods (Gao et al, 2020b). Extractive models Narayan et al, 2018;Luo et al, 2019;Xiao and Carenini, 2019) directly pick sentences from article and regard the aggregate of them as the summary.…”

Section: Related Workmentioning

confidence: 99%

VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

Li¹,

Chen²,

Gao³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Self Cite

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A popular multimedia news format nowadays is providing users with a lively video and a corresponding news article, which is employed by influential news media including CNN, BBC, and social media including Twitter and Weibo. In such a case, automatically choosing a proper cover frame of the video and generating an appropriate textual summary of the article can help editors save time, and readers make the decision more effectively. Hence, in this paper, we propose the task of Videobased Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem. The main challenge in this task is to jointly model the temporal dependency of video with semantic meaning of article. To this end, we propose a Dual-Interaction-based Multimodal Summarizer (DIMS), consisting of a dual interaction module and multimodal generator. In the dual interaction module, we propose a conditional self-attention mechanism that captures local semantic information within video and a global-attention mechanism that handles the semantic relationship between news text and video from a high level. Extensive experiments conducted on a large-scale real-world VMSMO dataset 1 show that DIMS achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. * Equal contribution. Ordering is decided by a coin flip.

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From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

Cited by 35 publications

References 8 publications

Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization

Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization

Meaningful Answer Generation of E-Commerce Question-Answering

VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

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